ContextLattice v3.2.3: Faster Agent Memory with Go/Rust Runtime and Staged Retrieval

I shipped ContextLattice v3.2.3, a local-first memory/context layer for apps and agents. I improved retrieval speed across all database backends and deep-source coverage for ContextLattice.

Key runtime design:

• Go handles ingress/orchestration
• Rust handles retrieval/memory hot paths
• interfaces with cli, desktop & web apps, claw/messaging platform-ready

Retrieval design:

• fast lane: topic_rollups + qdrant + postgres_pgvector
• deep continuation lane: mindsdb + mongo_raw + letta + memory_bank

Performance highlights:

• v3.2.0 Go lane mean 0.157s vs Python lane 0.255s (38.547% faster)
• earlier runtime cutover benchmark: ~4.94x faster mean vs legacy lane

Project:

• https://github.com/sheawinkler/ContextLattice
• https://contextlattice.io

Next phase:

• data compression, data expansion control
• interactive monitoring dashboard
• debugging agent & subagent orchestration challenges
• future:
  • decrease personal computer requirements
  • obsidian plug-in or similar/more performant knowledge visualizer